The following list is by far not exhaustive, you will be able to find more resources in the following:
- the Neuroimaging Informatics Tools and Resources Clearinghouse
- Mariam Aly's lab wiki
- Jonathan Peelle's list of resources for beignners
- Stephan Heunis has a list to many SPM and matlab material.
- https://github.com/brainhack101 also has a collections or links to courses, data...
- open neuroscience points to a lot of open things related to neuroscience.
- a list of Scientific Coding Resource put together by neuroconscience.
- Aya Ben-Yakov compiled a great list of open-science resources for the CBU in Cambridge.
- LabHacks is a list of resources for data driven neuroscientists put together by Patrick Beukema
- a list of [open computational neuroscience resources]( https://github.com/asoplata/open-computational-neuroscience-resources/blob/master/README.md) put together by Austin Soplata
- a list of Computational resources put together by Martina Vilas
- Brainhacking by Cameron Craddock within Neurohackademy 2018 (59 min)
- Craddock, R. C., Margulies, D. S., Bellec, P., Nichols, B. N., Alcauter, S., Barrios, F. A., … Xu, T. (2016). Brainhack: a collaborative workshop for the open neuroscience community. GigaScience, 5(1).
- Introduction to Brainhack by Cameron Craddock within Brainhack Proceedings 2017 (30 min)
- Introduction to Neurohackweek 2017 by Ariel Rokem within Neurohackweek 2017
- The Open Science MOOC
- Panel discussion: fostering open communities within Neurohackademy 2018 (1 hr 30 min)
- Science: open for all by Kirstie Whitaker within Neurohackademy 2018
- Surviving and thriving as an open scientist by Tal Yarkoni within Neurohackweek 2016
- MRC Cognition and Brain Sciences Unit Open Science Day 2018 Here
- Docker for scientists 1 by Chris Gorgolewski within Neurohackweek 2016 (1 hr 13min)
- Docker for scientists 2 by Chris Gorgolewski within Neurohackweek
- Docker tutorial by Lucy Owen within MIND 2018 (20 min)
- Neurodocker allows you to easily create containers suited to your neuroimaging needs. Here is a tutorial on how to use it.
- From interactive exploration to reproducible data science: Jupyter, Binder, Travis and friends. by Fernando Perez within Neurohackademy 2018 (1 hr 25 min)
- Jupyter tutorial by Eshin Jolly within MIND 2018 (31 min)
- Allen Institute Data and Software by Nicolas Cain within Neurohackweek 2017 (53 min)
- Allen Institute Datasets by Terri Gilbert within Neurohackweek 2016 (1 hr 8min)
- Allen Institute RNAseq data by Jeremy Miller within Neurohackweek 2016 (52 min)
- AllenSDK and the Allen Brain Observatory by Nicolas Cain and Justin Kiggins within Neurohackademy 2018 (1 hr 42 min)
- Integrating Allen Institute Datasets with MRI data by Kirstie Whitaker within Neurohackweek 2016 (28 min)
If you want to share data but your colleagues argue against it:
- Tor Wager's and Martin Lindquist's 2 parts MOOC on neuroimaging (part 1 and part 2)
Be sure to check the newly formed Neuroimaging quality control task force
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MRIQC MRI quality control. A BIDS app that runs a pipeline to assess the quality of your data.
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the PCP Quality Assessment Protocol is another BIDS app based on the protocol of [the connectome project data}(http://preprocessed-connectomes-project.org/quality-assessment-protocol/)
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Qoala-t for QA for freesurfer segmentations also with an online shinyapp
- Advanced time-series analysis (Dynamic Mode Decomposition) by Bing Brunton within Neurohackweek 2017 (1 hr 1 min)
- Advanced time-series analysis by Bing Brunton within Neurohackweek 2016 (1 hr 8 min)
- Data visualization by Tal Yarkoni within Neurohackademy 2018
- Efficiency and Design Optimization for fMRI by Jeanette Mumford within Neurohackweek 2017 (51 min)
- Image processing by Ariel Rokem within Neurohackweek 2016
- Modeling fMRI data by Kendrick Kay within Neurohackweek 2016 (1 hr 2 min)
- NiBabel 101 by Dan Lurie within Brainhack Americas (18 min)
- Numerical computing for neuroimaging by JB Poline within Neurohackademy 2018
- R for statistical analysis of fMRI data by Tara Madhyastha within Neurohackademy 2018 (1 hr 26 min)
- A video series on dynamic causal modeling by Kevin Aquino [6 hrs]
- A blog post tutorial on active inference (to get a better grasp on what free energy is)
- Cloud Computing for Neuroimaging 1 by Amanda Tan and Ariel Rokem within Neurohackademy 2018 (3 hr 9 min)
- Cloud Computing for Neuroimaging 2 by Tara Madhyastha within Neurohackweek 2016
- Using cloud computing for neuroimaging by Cameron Craddock within Neurohackweek 2016
- ReproNim is a good site to get up to date on doing reproducible neuroimaging research.
- Advance Unix and Make by Valentina Staneva and Tara Madhyastha within Neurohackweek 2016
- CRN resources by Chris Gorgolewski within Neurohackweek
- Improving the Reproducibility of Neuroimaging Research by Russ Poldrack within Neurohackweek 2016 (1 hr 23 min)
- GNU Make for Neuroimaging Workflows by Tara Madhyastha within Neurohackweek 2016 (48 min)
- Introduction to web technologies by Anisha Keshavan within Neurohackademy 2018 (56 min)
- Neuroimaging pipelines by Satra Ghosh within Neurohackweek 2017 (1 hr 33 min)
- Reproducibility in fMRI: What is the problem? 1 by Russ Poldrack within Neurohackweek 2017 (1 hr 40 min)
- Reproducibility in fMRI: What is the problem? 2 by Russ Poldrack within Neurohackweek 2017
- Same Data - Different Software - Different Results? Analytic Variability of Group fMRI Results by Alexander Bowring (12 mins)
- Reproducible research pipelines by Chris Gorgolewski and Satra Ghosh within Neurohackweek 2016
- Software pipelines for reproducible neuroimaging by Satra Ghosh and Chris Gorgolewski within Neurohackademy 2016 (1 hr 14 min)
- Neuroimaging Workflows & Statistics for reproducibility by Dorota Jarecka, Satrajit Ghosh, Celia Greenwood and Jean-Baptiste Poline at OHBM (3 hr 45 min)
- Tools from the Center for Open Neuroscience by Yaroslav Halchenko within MIND 2018
- Code-ocean is web based service that uses docker containers to let you run your analysis online. There is post by Stephan Heunis describing how he did that with an SPM pipeline.
- The Brain Imaging Data Structure (BIDS) presented by Chris Gorgolewski within Neurohackademy 2018 (56 min)
- the BIDS website
- the BIDS apps
- the BIDS starter kit
The Dmipy software project is dedicated to fasciliting high-level, reproducible diffusion microstructure research.
- The Dmipy open-source repository with many examples on implementing and fitting microstructure models.
- Our Preliminary Reference Paper
- Computer Vision by Michael Beyeler within Neurohackademy 2018 (53 min)
- Finding low-dimensional structure in large-scale neural recordings by Eva Dyer within Neurohackademy 2018 (1 hr 36 min)
- Interactive Data Visualization with D3 by Anisha Keshavan within Neurohackweek 2017 (49 min)
- Neuroethics by Eran Klein within Neurohackademy 2018 (58 min)
- Quantitative and diffusion MRI modeling of developmental data by Yason Yeatman within Neurohackweek
- P-values and reproducibility issues by JB Poline within Neurohackademy 2018 (1 hr 1 min)
- The evil p value by JB Poline within Neurohackweek 2017 (1 hr 2 min)
- Statistical Decision Theory by Joshua Vogelstein within Brainhack-Vienna (starts at 8 min, ends at 48 min)
- A reminder on how random field theory is used to correct for multiple comparison here.
- A primer on permutation testing (not only) for MVPA by Carsten Allefeld at OHBM 2018 (36 min)
- Cross-validation : what, which and how? by Pradeep Reedy Raamana at OHBM 2018 (30 min)
- Daniel Lakens MOOC on coursera on how to improve your statistical inferences
- Statistical thinking for the 21st century by Russ Poldrack: "I am trained as a psychologist and neuroscientist, not a statistician. However, my research on brain imaging for the last 20 years has required the use of sophisticated statistical and computational tools, and this has required me to teach myself many of the fundamental concepts of statistics. Thus, I think that I have a solid feel for what kinds of statistical methods are important in the scientific trenches."
A list of R based web based apps from shiny apps and R psychologist to help better understand:
- p-values
- confidence intervals
- p curves and why with a decent power and a large effect size, it is relatively unlikely to find a value between p<.01 and p<.05
- null hypothesis significance testing
- p hacking
- positive predictive values
- Overview_of_Meta-Analysis_Approaches by Tom Nichols at OHBM 2018 with slides (18 min)
- ALE and BrainMap by Simon B. Eickhoff at OHBM 2018 (22 min)
NiMARE is a Python library for coordinate- and image-based meta-analysis. Chris Gorgolewski wrote a tutorial on how to use it.
For coordinate based meta-analysis:
For image based meta-analysis:
- IBMA is the Image-Based Meta-Analysis toolbox for SPM.
Either on youtube or on some other platform
Lecture series on neuroimaging and electrophysiology from the Neurohackademy summer school.
Mike Cohen's lecturelets on time series data analysis here.
Jeanette Mumford series of videos on neuroimaging analysis on youtube. The channel also has Facebook group.
Here for the videos with 'tutorials' for FSL, SPM, Freesurfer and AFNI amongst other things.
The videos of the lectures and workshops from the previous HBM conferences are available online here.
This conference has the videos from its first edition here